Windows Media和RealSystem中组播流的实现及其比较

组播是一种可以充分节省带宽的数据传输方式,而流媒体是目前Internet上使用了流式传输技术的媒体文件。本文主要讨论了如何在Windows Media和RealSystem中用组播方式来传输流媒体信息,并在文章最后进行了比较和总结。

通过交叉验证堆栈和VAD信息检测Windows代码注入

Windows 32/64位代码注入攻击是恶意软件常用的攻击技术,在内存取证领域,现存的代码注入攻击检测技术在验证完整性方面不能处理动态内容,并且在解析内存中数据结构方面无法兼容不同版本的Windows系统。因此提出了通过交叉验证进程堆栈和VAD信息定位注入代码方法,将基于遍历栈帧得到的函数返回地址、模块名等信息结合进程VAD结构来检测函数返回地址、匹配文件名以定位注入代码,并且研发了基于Volatility取证框架的Windows代码注入攻击检测插件codefind。测试结果表明,即使在VAD节点被恶意软件修改,方法仍能够有效定位Windows 32/64位注入代码攻击。

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

Building Hot Snapshot Copy Based on Windows File System

This paper describes a method for building hot snapshot copy based on windows-file system （HSCF）. The architecture and running mechanism of HSCF are discussed after giving a comparison with other on-line backup tecbnology. HSCF, based on a file system filter driver, protects computer data and ensures their integrity and consistency with following three steps： access to open files, synchronization and copy on-write. Its strategies for improving system performance are analyzed including priority setting, incremental snapshot and load balance. HSCF is a new kind of snapshot technology to solve the data integrity and consistency problem in online backup, which is different from other storage-level snapshot and Open File Solution.

The research of optical windows used in aircraft sensor systems

The optical windows used in aircrafts protect their imaging sensors from environmental effects. Considering the imaging performance, flat surfaces are traditionally used in the design of optical windows. For aircrafts operating at high speeds, the optical windows should be relatively aerodynamic, but a flat optical window may introduce unacceptably high drag to the airframes. The linear scanning infrared sensors used in aircrafts with, respectively, a flat window, a spherical window and a toric window in front of the aircraft sensors are designed and compared. Simulation results show that the optical design using a toric surface has the integrated advantages of field of regard, aerodynamic drag, narcissus effect, and imaging performance, so the optical window with a toric surface is demonstrated to be suited for this application.

PERIODIC WINDOWS OF NONLINEAR GEAR SYSTEM BASED ON SYMBOLIC DYNAMICS

The periodic window is researched by means of the symbolic dynamics and formal language. Firstly, the proper sampling period is taken and the orbital points of periodic motion are obtained through Poincar6 mapping. Secondly, according to the method of symbolic dynamics of one-dimensional discrete mapping, the symbolic sequence describing the periodic orbit is obtained. Finally, based on the symbolic sequence, the corresponding model of minimal finite automation is constructed and the entropy is obtained by calculating the maximal eigenvalue of Stefan matrix. The results show that the orbits in periodic windows can be strictly marked by using the method of symbolic dynamics, thus a foundation for control of switching between target orbits is provided.

Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

Windows and Fieldbus Based Software Computer Numerical Control System

Computer numerical control(CNC)system is the base of modern digital and intelligent manufacturing technolo- gy.And opened its architecture and constituted based on PC and Windows operating system(OS)is the main trend of CNC sys- tem.However,even if the highest system priority is used in user mode,real-time capability of Windows(2000,NT,XP)for applications is not guaranteed.By using a device driver,which is running in kernel mode,the real time performance of Windows can be enhanced greatly.The acknowledgment performance of Windows to peripheral interrupts was evaluated.Harmonized with an intelligent real-time serial communication bus(RTSB),strict real-time performance can be achieved in Windows platform. An opened architecture software CNC system which is hardware independence is proposed based on PC and RTSB.A numerical control real time kernel(NCRTK),which is implemented as a device driver on Windows,is used to perform the NC tasks.Tasks are divided into real-time and non real-time.Real-time task is running in kernel mode and non real-time task is running in user mode.Data are exchanged between kernel and user mode by DMA and Windows Messages.

Bimetallic In_(2)O_(3)/Bi_(2)O_(3) Catalysts Enable Highly Selective CO_(2) Electroreduction to Formate within Ultra-Broad Potential Windows

CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet the industrial demands.Herein,the nanorod-like bimetallic ln_(2)O_(3)/Bi_(2)O_(3)catalysts were successfully synthesized by pyrolysis of bimetallic InBi-MOF precursors.The abundant oxygen vacancies generated from the lattice mismatch of Bi_(2)O_(3)and ln_(2)O_(3)reduced the activation energy of CO_(2)to*CO_(2)·^(-)and improved the selectivity of*CO_(2)·^(-)to formate simultaneously.Meanwhile,the carbon skeleton derived from the pyrolysis of organic framework of InBi-MOF provided a conductive network to accelerate the electrons transmission.The catalyst exhibited an ultra-broad applied potential window of 1200 mV(from-0.4 to-1.6 V vs RHE),relativistic high Faradaic efficiency of formate(99.92%)and satisfactory stability after 30 h.The in situ FT-IR experiment and DFT calculation verified that the abundant oxygen vacancies on the surface of catalysts can easily absorb CO_(2)molecules,and oxygen vacancy path is dominant pathway.This work provides a convenient method to construct high-performance bimetallic catalysts for the industrial application of CO_(2)RR.

A NOVEL TRACKING ALGORITHM BASED ON GRAVITY OF ENERGY WINDOWS FOR DS/CDMA SYSTEM

PN (Pseudo-Noise) code tracking is the most challenging task in a Direct Sequence Spread Code Division Multiplex Access (DS-CDMA) for celluar mobile communication systems. In this paper, the gravity of energy windows (GEW) tracking loop of the time and frequency uncertainty of the received signal is investigated, and the GEW’s analytical results in a multi-path fading channel are introduced. GEW tracking loop exploits the inherent multi-path diversity of the channel, and has better performance than single-path one when working in multi-path fading environment.

Left-Handedness with Three Zero-Absorption Windows Tuned by the Incoherent Pumping Field and Inter-Dot Tunnelings in a GaAs/AlGaAs Triple Quantum Dots System

Left-handedness with three zero-absorption windows is achieved in a triple-quantum-dot system. With the typ- ical parameters of a GaAs/AlGaAs heterostructure, the simultaneous negative relative electric permittivity and magnetic permeability are obtained by the adjustable incoherent pumping field and two inter-dot tunnelings. Furthermore, three zero-absorption windows in the left-handedness frequency bands are observed. The left- handedness with zero-absorption in the solid state heterostrueture may solve the challenges not only in the left-handed materials achieved by the photonic resonant scheme but also in the application of negative refractive materials with a large amount of absorption.

基于Windows Server Failover Cluster的Apache故障转移集群应用实践

在当前互联网时代,Web应用程序的高可用性和可靠性是确保业务持续运行的关键因素。针对Apache Web服务器,介绍了利用Windows Server 2019 Failover Cluster构建Apache故障转移集群的实践方案。通过配置两个节点的故障转移集群,当某个节点发生故障时,Apache服务将自动在另一个节点上重新启动,从而最大程度地减少系统中断的时间,提高Web应用的可用性。详细阐述了集群搭建的具体步骤、配置要点、集群验证方法以及运行时的监控与维护方法,为构建高可用Apache Web服务器提供一种实用的解决方案。

Microsoft Systems Management Server（SMS）OS Deployment Feature Pack使用的Windows Image（WIM）格式是Windows Vista要使用的最终版本吗？

不是的。SMS OS Deployment Feature Pack使用的WIM格式可以认为是WIM格式的0．9版，这种格式的1.0版随着Vista一起发布。SMS 4．0（现在称之为SCCM2007）完全支持Vista WIM（1．0）格式，

AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.